Noise is often present in acquired diagnostic images, such as those obtained from computed tomography (CT) scanning and other x-ray systems, and can be a significant factor in how well real intensity interfaces and fine details are preserved in the image. In addition to influencing diagnostic functions, noise also affects many automated image processing and analysis tasks that are crucial in a number of applications.
Methods for improving signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) can be broadly divided into two categories: those based on image acquisition techniques and those based on post-acquisition image processing. Improving image acquisition techniques beyond a certain point can introduce other problems and generally requires increasing the overall acquisition time. This risks delivering a higher X-ray dose to the patient and loss of spatial resolution and may require the expense of a scanner upgrade.
Post-acquisition filtering, an off-line image processing approach, is often as effective as improving image acquisition without affecting spatial resolution. If properly designed, post-acquisition filtering requires less time and is usually less expensive than attempts to improve image acquisition. Filtering techniques can be classified into two groupings: (i) enhancement, wherein wanted (structure) information is enhanced, hopefully without affecting unwanted (noise) information, and (ii) suppression, wherein unwanted information (noise) is suppressed, hopefully without affecting wanted information. Suppressive filtering operations may be further divided into two classes: a) space-invariant filtering, and b) space-variant filtering.
Space-invariant filtering techniques, wherein spatially independent fixed smoothing operations are carried out over the entire image, can be effective in reducing noise, but often blur important structures or features within the image at the same time. This can be especially troublesome because details of particular interest often lie along an edge or a boundary of a structure within the image, which can be blurred by conventional smoothing operations.
Various methods using space-variant filtering, wherein the smoothing operation is modified by local image features, have been proposed. Diffusive filtering methods based on Perona and Malik's work (1990) [Perona and Malik, “Scale-space and edge detection using anisotropic diffusion”, IEEE Trans. Pattern Anal. Machine Intelligence, 1990 vol. 12, pp. 629-639] have been adapted to a number of image filtering applications. Using these methods, image intensity at a pixel is diffused to neighboring pixels in an iterative manner, with the diffusion conductance controlled by a constant intensity gradient for the full image. The approach described by Perona and Malik uses techniques that preserve well-defined edges, but apply conventional diffusion to other areas of the 2-D image. While such an approach exhibits some success with 2-D images, however, there are drawbacks. One shortcoming of this type of solution relates to the lack of image-dependent guidance for selecting a suitable gradient magnitude. More importantly, since morphological or structural information is not used to locally control the extent of diffusion in different regions, fine structures often disappear and boundaries that are initially somewhat fuzzy may be further blurred upon filtering when this technique is used.
Three-dimensional imaging introduces further complexity to the problem of noise suppression. In cone-beam CT scanning, for example, a 3-D image is reconstructed from numerous individual scans, whose image data is aligned and processed in order to generate and present data as a collection of volume pixels or voxels. Using conventional diffusion techniques to reduce image noise can often blur significant features within the 3-D image, making it disadvantageous to perform more than rudimentary image clean-up for reducing noise content.
Thus, it is seen that there is a need for improved noise suppression filtering methods that reduce image noise without compromising sharpness and detail for significant structures or features in the image.